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Research Machine Learning Federated Learning Jobs

Responsibilities : • Design, train, and optimize machine learning models including LLMs ... federated learning • Experience contributing to academic publications, patents, or open-source ML ...

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... AI models from research prototypes into scalable, deployable systems used in real world ... Preferred : • Experience with edge AI, federated learning, or offline inference systems. • ...

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... AI models from research prototypes into scalable, deployable systems used in real world ... Preferred : • Experience with edge AI, federated learning, or offline inference systems. • ...

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

... AI models from research prototypes into scalable, deployable systems used in real world ... Preferred : • Experience with edge AI, federated learning, or offline inference systems. • ...

... Machine Learning, and more? We're thrilled to announce our PhD Research Internship Program, where ... Learning - Federated Learning / Differential Privacy - Red Teaming - ML Ops - LLM Ops ...

... machine-learning algorithms (e.g., differential privacy, secure aggregation, federated learning ... Have hands-on research or production experience with PETs. * Are fluent in modern deep-learning ...

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Research Machine Learning Federated Learning information

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$25.5K

$42.6K

$88K

How much do research machine learning federated learning jobs pay per year?

As of Sep 1, 2026, the average yearly pay for research machine learning federated learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What is a researcher in machine learning federated learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What are the key skills and qualifications needed to thrive as a researcher in machine learning federated learning?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

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What job categories do people searching Research Machine Learning Federated Learning jobs look for?

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Infographic showing various Research Machine Learning Federated Learning job openings in the United States as of August 2026, with employment types broken down into 90% Full Time, and 10% Contract. Highlights an 100% In-person job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Principal Investigator ? Transportation Research

Cervello Global Corporation

Bozeman, MT • On-site

Full-time

Re-posted 28 days ago


Job description

Cervello Global Research Corporation (CGRC) is a Service-Disabled Veteran-Owned Small Business headquartered in Omaha, Nebraska, operating as the R&D and proposal arm of the Cervello enterprise. CGRC holds an exclusive right-to-use license to STRATUM-X™, a cognitive orchestration and global transportation management platform with operational deployments across six countries. 

CGRC is seeking a Principal Investigator (PI) to lead the research and technical execution of a U.S. Department of Transportation (DOT) SBIR Phase I award under Topic 26-OS2, Freight Corridor Predictive Intelligence.

The selected PI will lead proof-of-concept development of STRATUM-X™ Corridor Intelligence—a predictive freight bottleneck forecasting system fusing real-time edge analytics, machine learning, generative AI, and federated learning to deliver actionable corridor-level intelligence for state and local DOTs and private freight operators. The program is executed in partnership with Montana State University’s National Security Research & Education (INSRE).

Key Responsibilities:

The PI will lead the following core research activities:

  • Design a predictive AI system architecture integrating edge devices, cloud analytics, and multimodal freight data sources.
  • Build and validate a proof-of-concept predictive model for short-term freight corridor performance forecasting.
  • Coordinate federated learning development with Montana State University / INSRE.
  • Define performance metrics, identify Phase II pilot corridors, and assess commercialization pathways.
  • Author the Phase I final report in accordance with DOT SBIR requirements.

NOTE: Position is contingent upon Contract Award